Detailed Survival analyis of the Survival lung data.

Libraries

library(survival)
library(FRESA.CAD)
## Loading required package: Rcpp
## Loading required package: stringr
## Loading required package: miscTools
## Loading required package: Hmisc
## 
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
## 
##     format.pval, units
## Loading required package: pROC
## Type 'citation("pROC")' for a citation.
## 
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)

Libraries

data(lung)
## Warning in data(lung): data set 'lung' not found
lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]

pander::pander(table(lung$status))
0 1
47 121 
pander::pander(summary(lung$time))
Min. 1st Qu. Median Mean 3rd Qu. Max.
5 175 268 310 416 1022

Exploring Raw Features with RRPlot

convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)
age wt.loss
0.106 0.106 
topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
  RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
                              atRate=c(0.90),
                  timetoEvent=lung$time,
                  title=topf,
#                  plotRR=FALSE
                  )
  idx <- idx + 1
}

names(RRanalysis) <- topFive

Reporting the Metrics

ROCAUC <- NULL
CstatCI <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL

for (topf in topFive)
{
  CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
  LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
  Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
  Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
  ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive

pander::pander(ROCAUC)
  est lower upper
age 0.591 0.494 0.688
wt.loss 0.556 0.458 0.654 
pander::pander(CstatCI)
  mean.C Index median lower upper
age 0.560 0.560 0.500 0.616
wt.loss 0.512 0.514 0.457 0.576 
pander::pander(LogRangp)
age 0.919
wt.loss 0.358 
pander::pander(Sensitivity)
  est lower upper
age 0.1157 0.0647 0.187
wt.loss 0.0496 0.0184 0.105 
pander::pander(Specificity)
  est lower upper
age 0.872 0.743 0.952
wt.loss 0.894 0.769 0.965 
meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe")
pander::pander(meanMatrix)
  ROCAUC C-Stat Sen Spe
age 0.591 0.560 0.1157 0.872
wt.loss 0.556 0.512 0.0496 0.894

Modeling

ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)

[+++++++-++++++++++++++++++++]..

sm <- summary(ml)
pander::pander(sm$coefficients)
Table continues below
  Estimate lower HR upper u.Accuracy r.Accuracy
ph.ecog 4.32e-01 1.194 1.541 1.988 0.679 0.649
sex -4.59e-01 0.456 0.632 0.876 0.649 0.679
pat.karno -1.77e-03 0.997 0.998 1.000 0.506 0.720
ph.karno -2.90e-07 1.000 1.000 1.000 0.577 0.720
age 9.13e-08 1.000 1.000 1.000 0.565 0.720
Table continues below
  full.Accuracy u.AUC r.AUC full.AUC IDI NRI
ph.ecog 0.601 0.601 0.620 0.600 0.0449 0.405
sex 0.601 0.620 0.601 0.600 0.0285 0.478
pat.karno 0.506 0.585 0.500 0.585 0.0292 0.342
ph.karno 0.577 0.570 0.500 0.570 0.0143 0.280
age 0.565 0.549 0.500 0.549 0.0162 0.195
  z.IDI z.NRI Delta.AUC Frequency
ph.ecog 3.33 2.48 -0.02005 1.0
sex 2.76 2.85 -0.00167 1.0
pat.karno 2.44 2.24 0.08546 1.0
ph.karno 2.22 1.64 0.06998 0.5
age 1.97 1.14 0.04871 0.2

Cox Model Performance

Here we evaluate the model using the RRPlot() function.

The evaluation of the raw Cox model with RRPlot()

Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years

timeinterval <- 2*mean(subset(lung,status==1)$time)

h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")
Initial Parameters
h0 timeinterval
0.85 578 
index <- predict(ml,lung)

rdata <- cbind(lung$status,ppoisGzero(index,h0))

rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Raw Train: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

By Risk Categories

obsexp <- rrAnalysisTrain$OERatio$atThrEstimates
pander::pander(obsexp)
  Observed L.CI H.CI Expected pvalue
Total 121 100.4 144.6 77.3 3.70e-06
low 83 66.1 102.9 51.6 4.96e-05
90% 38 26.9 52.2 25.9 2.33e-02 
maxx <- 1.1*max(c(obsexp$Observed,obsexp$Expected))
minx <- 0.9*min(c(obsexp$Observed,obsexp$Expected))

plot(obsexp$Expected,obsexp$Observed,
     xlim=c(minx,maxx),
     ylim=c(minx,maxx),
     main="Cal. Expected vs Observed",
     ylab="Observed",
     xlab="Expected",
     col=rainbow(nrow(obsexp)),
     log="xy")

errbar(obsexp$Expected,obsexp$Observed,obsexp$L.CI,obsexp$H.CI,add=TRUE,pch=0,errbar.col=rainbow(nrow(obsexp)),cex=0.75)
lines(x=c(1,maxx),y=c(1,maxx),lty=2)
text(obsexp$Expected,obsexp$Observed,rownames(obsexp),pos=2,cex=0.75)

Uncalibrated Performance Report

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.649 0.478 3.39e-01 3.39e-01 0.493
RR 1.214 1.742 6.85e+01 6.85e+01 1.270
RR_LCI 1.014 1.260 1.44e-01 1.44e-01 1.037
RR_UCI 1.454 2.408 3.26e+04 3.26e+04 1.555
SEN 0.314 0.826 1.00e+00 1.00e+00 0.612
SPE 0.830 0.511 1.91e-01 1.91e-01 0.596
BACC 0.572 0.669 5.96e-01 5.96e-01 0.604
NetBenefit 0.138 0.470 6.04e-01 6.04e-01 0.331 
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.57 1.3 1.87 3.7e-06 
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.23 1.23 1.19 1.27 
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
1.27 1.27 1.26 1.29 
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.651 0.651 0.591 0.714 
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.691 0.598 0.784 
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.314 0.233 0.405 
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.83 0.692 0.924 
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.648 
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 122 83 95.6 1.65 7.95
class=1 46 38 25.4 6.22 7.95

Cox Calibration

op <- par(no.readonly = TRUE)


calprob <- CoxRiskCalibration(ml,lung,"status","time")

( 849.0447 , 781.9918 , 1.468558 , 121 , 140.6966 )

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
1.55 1.83 849

The RRplot() of the calibrated model

h0 <- calprob$h0
timeinterval <- calprob$timeInterval;

rdata <- cbind(lung$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Train Cal: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

By Risk Categories

obsexp <- rrAnalysisTrain$OERatio$atThrEstimates
pander::pander(obsexp)
  Observed L.CI H.CI Expected pvalue
Total 121 100.4 144.6 96.2 0.0142
low 83 66.1 102.9 64.2 0.0244
90% 38 26.9 52.2 32.3 0.2912 
maxx <- 1.1*max(c(obsexp$Observed,obsexp$Expected))
minx <- 0.9*min(c(obsexp$Observed,obsexp$Expected))

plot(obsexp$Expected,obsexp$Observed,
     xlim=c(minx,maxx),
     ylim=c(minx,maxx),
     main="Cal. Expected vs Observed",
     ylab="Observed",
     xlab="Expected",
     col=rainbow(nrow(obsexp)),
     log="xy")

errbar(obsexp$Expected,obsexp$Observed,obsexp$L.CI,obsexp$H.CI,add=TRUE,pch=0,errbar.col=rainbow(nrow(obsexp)),cex=0.75)
lines(x=c(1,maxx),y=c(1,maxx),lty=2)
text(obsexp$Expected,obsexp$Observed,rownames(obsexp),pos=2,cex=0.75)

Calibrated Train Performance

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8523 0.695 5.31e-01 5.31e-01 5.31e-01
RR 1.2142 1.742 6.85e+01 6.85e+01 6.85e+01
RR_LCI 1.0143 1.260 1.44e-01 1.44e-01 1.44e-01
RR_UCI 1.4536 2.408 3.26e+04 3.26e+04 3.26e+04
SEN 0.3140 0.826 1.00e+00 1.00e+00 1.00e+00
SPE 0.8298 0.511 1.91e-01 1.91e-01 1.91e-01
BACC 0.5719 0.669 5.96e-01 5.96e-01 5.96e-01
NetBenefit -0.0484 0.283 4.64e-01 4.64e-01 4.64e-01 
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.26 1.04 1.5 0.0142 
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
0.99 0.99 0.957 1.02 
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
1.02 1.02 1.01 1.02 
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.651 0.653 0.596 0.711 
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.691 0.598 0.784 
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.314 0.233 0.405 
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.83 0.692 0.924 
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.851 
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 122 83 95.6 1.65 7.95
class=1 46 38 25.4 6.22 7.95

Cross-Validation

rcv <- randomCV(theData=lung,
                theOutcome = Surv(time,status)~1,
                fittingFunction=BSWiMS.model, 
                trainFraction = 0.95,
                repetitions=200,
                classSamplingType = "Pro"
         )

.[++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[+++].[++]10 Tested: 75 Avg. Selected: 3.8 Min Tests: 1 Max Tests: 4 Mean Tests: 1.333333 . MAD: 0.4685681

.[+-].[+++].[+++].[+++].[+++].[+++].[++].[+++].[++++].[+++]20 Tested: 112 Avg. Selected: 3.8 Min Tests: 1 Max Tests: 8 Mean Tests: 1.785714 . MAD: 0.473474

.[+++].[+++].[++].[+++].[+++].[+++].[+++].[+].[+++].[++]30 Tested: 135 Avg. Selected: 3.733333 Min Tests: 1 Max Tests: 8 Mean Tests: 2.222222 . MAD: 0.4740328

.[+++].[+++].[+++].[+++].[+++].[+++].[+++].[++++].[+++].[+++]40 Tested: 150 Avg. Selected: 3.825 Min Tests: 1 Max Tests: 9 Mean Tests: 2.666667 . MAD: 0.4769031

.[++].[++].[++].[+++].[++-].[++].[++].[+].[+++].[++]50 Tested: 157 Avg. Selected: 3.68 Min Tests: 1 Max Tests: 12 Mean Tests: 3.184713 . MAD: 0.4743397

.[+++].[++].[++].[++].[+].[+++].[+++].[++].[++].[++]60 Tested: 165 Avg. Selected: 3.6 Min Tests: 1 Max Tests: 12 Mean Tests: 3.636364 . MAD: 0.4758089

.[++].[++].[+++].[+++].[+].[+++].[++].[++].[+++].[+++]70 Tested: 166 Avg. Selected: 3.571429 Min Tests: 1 Max Tests: 14 Mean Tests: 4.216867 . MAD: 0.4749926

.[+++].[+++].[++-].[++].[+++].[+++].[+++].[++].[+++].[++]80 Tested: 167 Avg. Selected: 3.575 Min Tests: 1 Max Tests: 14 Mean Tests: 4.790419 . MAD: 0.4745623

.[++-].[++].[+++].[+++].[++++].[+++].[++].[+++].[++].[++]90 Tested: 168 Avg. Selected: 3.577778 Min Tests: 1 Max Tests: 14 Mean Tests: 5.357143 . MAD: 0.4745711

.[++].[+++].[+++].[++].[+++].[+++].[+].[++-].[++].[++-]100 Tested: 168 Avg. Selected: 3.55 Min Tests: 1 Max Tests: 14 Mean Tests: 5.952381 . MAD: 0.474259

.[++++].[+++].[+++].[+++].[+++].[+++].[+++].[++].[+++].[++++]110 Tested: 168 Avg. Selected: 3.6 Min Tests: 1 Max Tests: 14 Mean Tests: 6.547619 . MAD: 0.4747092

.[+++].[++++].[++++].[+++].[+++].[+++].[+++].[+++].[++].[+++]120 Tested: 168 Avg. Selected: 3.641667 Min Tests: 2 Max Tests: 15 Mean Tests: 7.142857 . MAD: 0.4745077

.[+++].[++].[+++].[+-].[+++].[+++].[+++].[++].[+++].[+++]130 Tested: 168 Avg. Selected: 3.638462 Min Tests: 2 Max Tests: 15 Mean Tests: 7.738095 . MAD: 0.4745049

.[++].[++].[+++].[+++].[+++].[++].[+++].[+++].[++].[++]140 Tested: 168 Avg. Selected: 3.628571 Min Tests: 2 Max Tests: 15 Mean Tests: 8.333333 . MAD: 0.4744039

.[+++].[+++].[+++].[+++].[+++].[+++].[+++].[+].[++-].[++]150 Tested: 168 Avg. Selected: 3.626667 Min Tests: 2 Max Tests: 15 Mean Tests: 8.928571 . MAD: 0.4744466

.[++-].[++].[+++].[+++].[+++-].[+++].[+++].[++-].[++].[++]160 Tested: 168 Avg. Selected: 3.61875 Min Tests: 3 Max Tests: 17 Mean Tests: 9.52381 . MAD: 0.4744519

.[+++].[+++].[++-].[++].[+++].[++-].[+++].[+++].[+++].[++]170 Tested: 168 Avg. Selected: 3.617647 Min Tests: 3 Max Tests: 20 Mean Tests: 10.11905 . MAD: 0.4747196

.[++-].[++].[+++].[++].[++].[++].[+++].[++].[+].[+++]180 Tested: 168 Avg. Selected: 3.594444 Min Tests: 3 Max Tests: 20 Mean Tests: 10.71429 . MAD: 0.475085

.[++].[+++].[+++].[++].[++].[+++].[+++].[+++].[+++].[+++]190 Tested: 168 Avg. Selected: 3.6 Min Tests: 3 Max Tests: 21 Mean Tests: 11.30952 . MAD: 0.4752468

.[+++].[+++].[+++].[++].[++].[++-].[+++].[+++].[+++].[+++]200 Tested: 168 Avg. Selected: 3.605 Min Tests: 3 Max Tests: 22 Mean Tests: 11.90476 . MAD: 0.4753461

stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]

bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)

rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names

rrAnalysisTest <- RRPlot(rdatacv,atRate=c(0.90),
                     timetoEvent=times,
                     title="Test: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Cross-Validation Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8640 0.675 0.538 5.18e-01 5.18e-01
RR 1.1859 2.958 3.079 1.46e+01 1.46e+01
RR_LCI 0.9720 1.387 1.312 3.47e-02 3.47e-02
RR_UCI 1.4469 6.309 7.226 6.13e+03 6.13e+03
SEN 0.1983 0.959 0.967 1.00e+00 1.00e+00
SPE 0.8936 0.298 0.255 4.26e-02 4.26e-02
BACC 0.5460 0.628 0.611 5.21e-01 5.21e-01
NetBenefit -0.0461 0.283 0.454 4.33e-01 4.33e-01 
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.24 1.02 1.48 0.0229 
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
0.978 0.978 0.946 1.01 
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
0.977 0.977 0.969 0.985 
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.596 0.595 0.524 0.657 
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.61 0.51 0.711 
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.198 0.131 0.281 
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.894 0.769 0.965 
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.863 
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
Logrank test Chisq = 2.519766 on 1 degrees of freedom, p = 0.112427
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 139 97 103.1 0.367 2.52
class=1 29 24 17.9 2.118 2.52

Calibrating the test results

rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)

( 859.155 , 781.9918 , 1.486045 , 121 , 143.3226 )

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
0.85 1 859 
timeinterval <- calprob$timeInterval;

rdata <- cbind(status,calprob$prob)


rrAnalysisTest <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=times,
                     title="Calibrated Test: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Calibrated Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8640 0.675 0.538 5.18e-01 5.18e-01
RR 1.1859 2.958 3.079 1.46e+01 1.46e+01
RR_LCI 0.9720 1.387 1.312 3.47e-02 3.47e-02
RR_UCI 1.4469 6.309 7.226 6.13e+03 6.13e+03
SEN 0.1983 0.959 0.967 1.00e+00 1.00e+00
SPE 0.8936 0.298 0.255 4.26e-02 4.26e-02
BACC 0.5460 0.628 0.611 5.21e-01 5.21e-01
NetBenefit -0.0461 0.283 0.454 4.33e-01 4.33e-01 
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.25 1.04 1.49 0.0168 
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
0.989 0.99 0.957 1.02 
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
0.978 0.978 0.97 0.986 
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.596 0.596 0.535 0.661 
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.61 0.51 0.711 
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.198 0.131 0.281 
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.894 0.769 0.965 
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.863 
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
Logrank test Chisq = 2.519766 on 1 degrees of freedom, p = 0.112427
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 139 97 103.1 0.367 2.52
class=1 29 24 17.9 2.118 2.52